The improved performance of machine learning often comes at the cost of interpretability. However, for security and biometrics applications, interpretation of algorithms is imperative. Not only this will enable us to assert that the model is learning actual significant information, but also it will act as a defense mechanism against spoofing or counterfeiting attacks. Recently, considerable focus has been put on interpreting the deep learning models, instead of considering them merely as black-boxes [1]}.
